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Found 28 papers, 3 papers with code
Weighted Training for Cross-Task Learning
In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks.
Finite Sample Analysis Of Dynamic Regression Parameter Learning
The proposed bounds depend on the shape of a certain spectrum related to the system operator, and thus provide the first known explicit geometric parameter of the data that can be used to bound estimation errors.
Multi Instance Learning For Unbalanced Data
In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective.
DEEP HIERARCHICAL MODEL FOR HIERARCHICAL SELECTIVE CLASSIFICATION AND ZERO SHOT LEARNING
The second, zero-shot learning problem deals with making reasonable inferences on novel classes.
A Better Resource Allocation Algorithm with Semi-Bandit Feedback
We study a sequential resource allocation problem between a fixed number of arms.
Efficient Loss-Based Decoding on Graphs For Extreme Classification
We build on a recent extreme classification framework with logarithmic time and space, and on a general approach for error correcting output coding (ECOC) with loss-based decoding, and introduce a flexible and efficient approach accompanied by theoretical bounds.
Rotting Bandits
In the classical MAB problem, a decision maker must choose an arm at each time step, upon which she receives a reward.
Bandits meet Computer Architecture: Designing a Smartly-allocated Cache
In many embedded systems, such as imaging sys- tems, the system has a single designated purpose, and same threads are executed repeatedly.
Linear Multi-Resource Allocation with Semi-Bandit Feedback
In each time step the learner chooses an allocation of several resource types between a number of tasks.
Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests
We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source.
CONQUER: Confusion Queried Online Bandit Learning
We present a new recommendation setting for picking out two items from a given set to be highlighted to a user, based on contextual input.
Belief Flows of Robust Online Learning
This paper introduces a new probabilistic model for online learning which dynamically incorporates information from stochastic gradients of an arbitrary loss function.
Learning Multiple Tasks in Parallel with a Shared Annotator
We introduce a new multi-task framework, in which $K$ online learners are sharing a single annotator with limited bandwidth.
Outlier-Robust Convex Segmentation
We derive a convex optimization problem for the task of segmenting sequential data, which explicitly treats presence of outliers.
Optimal Resource Allocation with Semi-Bandit Feedback
We study a sequential resource allocation problem involving a fixed number of recurring jobs.
Selective Sampling with Drift
Simulations on synthetic and real-world datasets demonstrate the superiority of our algorithms as a selective sampling algorithm in the drifting setting.
Advice-Efficient Prediction with Expert Advice
Advice-efficient prediction with expert advice (in analogy to label-efficient prediction) is a variant of prediction with expert advice game, where on each round of the game we are allowed to ask for advice of a limited number $M$ out of $N$ experts.
A Generalized Online Mirror Descent with Applications to Classification and Regression
A unifying perspective on the design and the analysis of online algorithms is provided by online mirror descent, a general prediction strategy from which most first-order algorithms can be obtained as special cases.
Volume Regularization for Binary Classification
We introduce a large-volume box classification for binary prediction, which maintains a subset of weight vectors, and specifically axis-aligned boxes.
Learning Multiple Tasks using Shared Hypotheses
In this work we consider a setting where we have a very large number of related tasks with few examples from each individual task.
Learning via Gaussian Herding
We introduce a new family of online learning algorithms based upon constraining the velocity flow over a distribution of weight vectors.
New Adaptive Algorithms for Online Classification
We propose a general framework to online learning for classification problems with time-varying potential functions in the adversarial setting.
Adaptive Regularization of Weight Vectors
We present AROW, a new online learning algorithm that combines several properties of successful : large margin training, confidence weighting, and the capacity to handle non-separable data.
Exact Convex Confidence-Weighted Learning
Confidence-weighted (CW) learning [6], an online learning method for linear classifiers, maintains a Gaussian distributions over weight vectors, with a covariance matrix that represents uncertainty about weights and correlations.
Learning Bounds for Domain Adaptation
Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain.
